As part of my MSc in finance, I have a "quantitative research method" course and we are using Eviews to make forecasts.

We have been given series of stock prices by the professor. He asked us to enter the stock prices under Eviews and generate the log by equation.

seriesname = log(seriesname)

However, I doubt this formula is correct. I tested it on excel and it only give the log of the price.

My guess is that we need stationary data to run the ARMA model. However, stock prices are not stationary. I think we should study the log returns of the prices. Because, if a stock prices grows by 2% each week, it is not stationary, but the return is stationary (i.e. linear at 2%).

So we should do LN(stockpricetime1/stockpricetime0) and then transfer the data into Eviews.

Am I right?

I spent two hours running the dickey fuller test to find out if my stock prices are stationnary, I only got the P value of the ADF < 0,05 using the ADF (modified Schwartz) at the 1st difference and with none of the trend and intercept.

I have other questions but I will wait until I solve this one ... thanks everybody

When you model a time serie y(t) as an ARMA process for instance. It assumes that your serie is stationary. Hence, this assumption needs to be true to apply further results in this area. The stock price x(t) is basically not a stationary quantity, but the log-return y(t) = ln(x(t)/x(t-1)) is one. Hence, this is the quantity you will work on, and you have some statistical tests to verify that y(t) is well a stationary serie.

My question is just: if we know that in general, a stock price is not stationary, is it correct to say that we'd better use the log returns (the variations of stock prices) as data in our table.